Menu
Calculate AI feature costs and challenge if you actually need it. Invokes ai-cost-analyzer agent for detailed economics modeling.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | ai-cost-check |
| description | Calculate AI feature costs and challenge if you actually need it. Invokes ai-cost-analyzer agent for detailed economics modeling. |
Before you build an AI feature, answer two questions:
Most PMs skip #1 and regret #2 later.
When this skill is invoked, start with:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
AI COST CHECK
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
AI features have marginal costs that scale with usage.
Model this BEFORE building, not after launch.
What AI feature are you considering?
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
/ai-cost-check [feature-name]
Examples:
/ai-cost-check "product recommendations" - Calculate recommendation costs/ai-cost-check "email composer" - Model email generation economics/ai-cost-check --compare - Compare cost across different modelsAI products have marginal costs that scale with usage. Every user interaction costs money. Model this BEFORE building, not after launch when you're hemorrhaging cash.
| AI Cost as % of Revenue | Status | Recommendation |
|---|---|---|
| <15% | Sustainable | Build it |
| 15-30% | Viable | Build with optimization plan |
| >30% | Unsustainable | Don't build (or fundamentally rethink) |
FEATURE DETAILS:
- Model: GPT-4 Turbo
- Calls per recommendation: 1
- Input: 1,500 tokens
- Output: 300 tokens
COST BREAKDOWN:
Per request: $0.024
Per user/month: $2.16
| Scale | Monthly Cost | Your Revenue | AI % of Revenue |
|-------|-------------|--------------|-----------------|
| 100 | $216 | $2,000 | 10.8% |
| 10K | $21,600 | $200,000 | 10.8% |
VERDICT: Sustainable at 10.8% of revenue
OPTIMIZATION PATHS:
1. Caching (saves 40-60%): $8,640/month at 10K users
2. Model selection (saves 70%): Use GPT-3.5 for simple cases
| Model | Input | Output |
|---|---|---|
| GPT-4 Turbo | $0.01/1K | $0.03/1K |
| GPT-4o | $0.005/1K | $0.015/1K |
| GPT-3.5 Turbo | $0.0005/1K | $0.0015/1K |
| Claude 3.5 Sonnet | $0.003/1K | $0.015/1K |
| Claude 3 Haiku | $0.00025/1K | $0.00125/1K |
/ai-health-check - Full pre-launch readiness audit/four-risks - Includes viability (business model) risk/pmf-survey - Validate willingness to payKey insight: "Most AI features are solutions looking for problems. Validate the problem before modeling costs."
Plan the v1→v2→v3 agency progression for AI features. Walk through mapping how autonomy increases over time, define promotion criteria, and generate artifacts for stakeholder alignment. Based on CC/CD framework.
Expert system for designing and architecting AI agent workflows based on proven Meta methodologies. Use when users need to build AI agents, create agent workflows, solve problems using agentic systems, integrate multiple tools into agent architectures, or need guidance on agent design patterns. Helps translate business problems into structured agent solutions with clear scope, tool integration, and multi-layer architecture planning.
Diagnose why an AI feature is underperforming, hallucinating, or behaving inconsistently. Uses 4D audit to work backwards from symptoms to root cause.
Pre-launch health check that blocks you from shipping broken AI features. Grades 6 dimensions (model selection, data quality, cost, monitoring, failure UX, optimization).
Post-launch AI feature calibration workflow. Document error patterns, review eval performance, and decide on agency promotion. Based on CC/CD framework for continuous calibration of AI products.
Apply Brian Balfour's CODER framework to drive organizational AI adoption. Constraints, Ownership, Directives, Expectations, Rewards.